与训练集损失不同，开发集损失总是在增加

Question

我为文本class化问题设计了一个网络。为此，我使用了 huggingface transformet 的 BERT 模型，其上方有一个线性层用于微调。我的问题是训练集上的损失正在减少，这很好，但是当在开发集上的每个时期之后进行评估时，损失随着时期的增加而增加。我正在发布我的代码以调查它是否有问题。

for epoch in range(1, args.epochs + 1):
    total_train_loss = 0
    trainer.set_train()

    for step, batch in enumerate(train_dataloader):
        loss = trainer.step(batch)
        total_train_loss += loss

    avg_train_loss = total_train_loss / len(train_dataloader)

    logger.info(('Training loss for epoch %d/%d: %4.2f') % (epoch, args.epochs, avg_train_loss))

    print("\n-------------------------------")
    logger.info('Start validation ...')
    trainer.set_eval()
    y_hat = list()
    y = list()
    total_dev_loss = 0
    for step, batch_val in enumerate(dev_dataloader):
        true_labels_ids, predicted_labels_ids, loss = trainer.validate(batch_val)
        total_dev_loss += loss
        y.extend(true_labels_ids)
        y_hat.extend(predicted_labels_ids)
    avg_dev_loss = total_dev_loss / len(dev_dataloader)
    print(("\n-Total dev loss: %4.2f on epoch %d/%d\n") % (avg_dev_loss, epoch, args.epochs))

print("Training terminated!")

以下是训练文件，我用它对给定的批次进行前向传播，然后进行相应的反向传播。

class Trainer(object):
    def __init__(self, args, model, device, data_points, is_test=False, train_stats=None):
        self.args = args
        self.model = model
        self.device = device
        self.loss = nn.CrossEntropyLoss(reduction='none')

        if is_test:
            # Should load the model from checkpoint
            self.model.eval()       
            self.model.load_state_dict(torch.load(args.saved_model))
            logger.info('Loaded saved model from %s' % args.saved_model)

        else:
            self.model.train()
            self.optim = AdamW(model.parameters(), lr=2e-5, eps=1e-8)
            total_steps = data_points * self.args.epochs
            self.scheduler = get_linear_schedule_with_warmup(self.optim, num_warmup_steps=0,
                                                             num_training_steps=total_steps)

    def step(self, batch):
        batch = tuple(t.to(self.device) for t in batch)
        batch_input_ids, batch_input_masks, batch_labels = batch
        self.model.zero_grad()
        outputs = self.model(batch_input_ids,
                             attention_mask=batch_input_masks,
                             labels=batch_labels)
        loss = self.loss(outputs, batch_labels)
        loss = loss.sum()
        (loss / loss.numel()).backward()
        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
        self.optim.step()
        self.scheduler.step()
        return loss

    def validate(self, batch):
        batch = tuple(t.to(self.device) for t in batch)
        batch_input_ids, batch_input_masks, batch_labels = batch
        with torch.no_grad():
            model_output = self.model(batch_input_ids,
                                 attention_mask=batch_input_masks,
                                 labels=batch_labels)

        predicted_label_ids = self._predict(model_output)
        label_ids = batch_labels.to('cpu').numpy()

        loss = self.loss(model_output, batch_labels)
        loss = loss.sum()

        return label_ids, predicted_label_ids, loss

    def _predict(self, logits):
        return np.argmax(logits.to('cpu').numpy(), axis=1)

最后，以下是我的模型（即分类器）class：

import torch.nn as nn
from transformers import BertModel


class Classifier(nn.Module):

    def __init__(self, args, is_eval=False):
        super(Classifier, self).__init__()

        self.bert_model = BertModel.from_pretrained(
            args.init_checkpoint,
            output_attentions=False,
            output_hidden_states=True,
        )
        self.is_eval_mode = is_eval
        self.linear = nn.Linear(768, 2) # binary classification

    def switch_state(self):
        self.is_eval_mode = not self.is_eval_mode

    def forward(self, input_ids, attention_mask=None, labels=None):

        bert_outputs = self.bert_model(input_ids,
                                       token_type_ids=None,
                                       attention_mask=attention_mask)

        # Should give the logits to the the linear layer
        model_output = self.linear(bert_outputs[1])

        return model_output

为了可视化整个时代的损失：

Answer 1

当验证损失增加时，表示您的模型过度拟合

Answer 2

当我使用 Bert 进行文本分类时，我的模型通常表现如您所说。这在一定程度上是预料之中的，因为 pre-trained 模型往往需要很少的 epochs 到 fine-tune，实际上如果你检查 Bert's paper，fine-tuning 推荐的 epochs 数在 2 到 4 之间。

另一方面，我通常只在 1 或 2 个 epoch 时找到最佳值，这也与你的情况一致。我的猜测是：在 fine-tuning pre-trained 模型适合下游任务和忘记在 pre-training 时学习的权重之间存在 trade-off。根据您拥有的数据，平衡点可能迟早会出现，之后就会开始过度拟合。但这段是根据我的经验推测的。

与训练集损失不同，开发集损失总是在增加

Loss on dev set is always increasing unlike training set loss

python

neural-network

deep-learning

pytorch

loss-function